摘要
提出基于经验模式分解技术的GPS高程时间序列时频分析方法,并以两个CORS基准站多年连续的高程时间序列数据为对象,获取序列信号的各个本征模态函数分量,研究了信号周期运动的主要贡献分量和序列趋势项的合成与分离的方法。结果表明:在一定的序列区间长度上识别周期项与趋势项才有意义;经验模式分解技术分离的序列趋势项呈现大曲率的非线性形式,与传统的周期函数高程序列拟合线性趋势项存在较大的差异。
The time-frequency analysis method base on EMD( empirical mode decomposition) for GPS height time series is put forward. Taking two national CORS stations' GPS height time series as an example, each intrinsic mode function is decomposed and the contribution rate of variance is also calculated. The problem of identifying fa- ther distinguishing the periodical signals and the trend is emphasizerd. The results indicate that it is only meaning- ful to distinguish the periodical and the trend signal on certain length of time series' section. The trend based on EMD or EEMD presents with large curvature while the traditional form of periodical fitting function only linear form.
出处
《大地测量与地球动力学》
CSCD
北大核心
2012年第3期129-134,共6页
Journal of Geodesy and Geodynamics
基金
国家基础测绘项目(B2551)
中国测绘科学研究院基本科研业务费资助
关键词
经验模式分解
本征模态函数
趋势项
GPS高程时间序列
方差贡献率
empirical mode decomposition(EMD)
intrinsic mode function
trend
GPS height time series
contribu-tion rate of variance